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Record W2896259415 · doi:10.1053/j.jrn.2018.08.006

Global Prevalence of Protein-Energy Wasting in Kidney Disease: A Meta-analysis of Contemporary Observational Studies From the International Society of Renal Nutrition and Metabolism

2018· review· en· W2896259415 on OpenAlex
Juan Jesús Carrero, Fridtjof Thomas, Kristóf Nagy, Fatiu A. Arogundade, Carla María Avesani, Maria Chan, Michael S. Chmielewski, A. C. C. Cordeiro, Ángeles Espinosa-Cuevas, Enrico Fiaccadori, Fitsum Guebre‐Egziabher, Rosa K. Hand, Adriana M. Hung, T. Alp İkizler, Lina Johansson, Kamyar Kalantar‐Zadeh, Tilakavati Karupaiah, Bengt Lindholm, Peter Marckmann, Denise Mafra, Rulan S. Parekh, Jongha Park, Sharon Russo, Anitá Saxena, Siren Sezer, Daniel Teta, Pieter M. ter Wee, Cecile Verseput, Angela Yee‐Moon Wang, Hong Xu, Yimin Lu, Miklos Z. Molnar, Csaba P. Kövesdy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Renal Nutrition · 2018
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoUniversity Health Network
FundersU.S. Department of Veterans Affairs
KeywordsMedicineWastingObservational studyMeta-analysisKidney diseaseIntensive care medicineDiseaseEnergy metabolismWasting SyndromeInternal medicineGerontologyPhysiologyEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.219
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.373
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it